Displaying a service graph in association with a time of a detected anomaly

ABSTRACT

Described embodiments provide systems and methods for displaying a service graph in association with a time of a detected anomaly. A device may store a plurality of snapshots of a service graph of a plurality of microservices. Each of the snapshots of the service graphs include metrics at a respective time increment from execution of each of the plurality of microservices. The device may detect an anomaly with operation of one or more microservices of the plurality of services. The device may identify a set of snapshots of the service graph within a predetermined time period of a time of the anomaly. The device may display each of the snapshots in the set of snapshots of in sequence corresponding to time increments within the predetermined time period of the time of the anomaly.

FIELD OF THE DISCLOSURE

The present application generally relates to service graphs, includingbut not limited to systems and methods for displaying a service graph inassociation with a time of a detected anomaly.

BACKGROUND

Various services may be used, accessed, or otherwise provided to users.Such services may include microservices which perform a subset of tasksor functions which, collectively, provide the service to the user. Somemicroservice(s) may be updated or replaced with new versions of themicroservice(s). In some instances, new versions of the microservice(s)may cause the service to not perform as desired. In some instances,increased network traffic may cause the service to not perform asdesired.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features, nor is it intended to limit the scope of the claimsincluded herewith.

Systems and methods for displaying a service graph in association with atime of a detected anomaly are discussed herein. A service graph may bea tool by which a service including various microservices correspondingthereto may be visualized. Such a tool may be used for network trafficmonitoring purposes, diagnostic purposes, troubleshooting purposes, andso forth. The service graph may depict various traffic volume, latency,error rates, and other metrics corresponding to the service. In someimplementations, one or more microservices of the service maymalfunction, fail, glitch, or otherwise experience a condition which isan anomaly. To correct such conditions, an administrator may identify acause of the condition. However, identification of the cause of thecondition may be difficult without understanding conditions of themicroservice(s) leading up to and/or following occurrence of theanomaly.

In some implementations, a device (such as an intermediary device) maystore a plurality of snapshots of a service graph of a plurality ofmicroservices generated for each of a plurality of time increments overa time period. The snapshots may include metrics at a respective timeincrement from execution of each of the microservices. The device maydetect an anomaly with operation of one or more of the microservices.The device may identify a set of snapshots of the service graph within apredetermined time period of a time of the anomaly. The device maydisplay each of a sequence of the snapshots in the set corresponding totime increments within the predetermined time period of the time of theanomaly.

Rather than a user (such as an administrator) manually evaluating themicroservice(s) which contributed to or caused the anomaly, theimplementations described herein may automatically detect the anomalyand provide snapshots of the service graph including metricscorresponding to the service graph. The administrator can thus quicklyand efficiently determine the cause of the anomaly based on the datafrom or represented in the service graph. The implementations describedherein may increase the efficiency of diagnostics of anomalies formicroservices corresponding to a service by providing a visual aid bywhich an administrator can observe metrics corresponding to themicroservice(s) prior to, during, and/or after the anomaly. Theimplementations described herein may decrease downtime as a result ofsuch anomalies by providing a faster mechanism by which an administratorcan remediate the cause of the anomaly.

An aspect provides a method for displaying a service graph inassociation with a time of a detected anomaly. The method includesstoring, by a device, a plurality of snapshots of a service graph of aplurality of microservices generated for each of a plurality timeincrements over a time period. Each of the plurality of snapshots of theservice graphs may include metrics at a respective time increment fromexecution of each of the plurality of microservices. The method includesdetecting, by the device, an anomaly with operation of one or moremicroservices of the plurality of services. The method includesidentifying, by the device, a set of snapshots from the plurality ofsnapshots of the service graph within a predetermined time period of atime of the anomaly. The method includes displaying, by the device, eachof the snapshots in the set of snapshots of in sequence corresponding totime increments within the predetermined time period of the time of theanomaly.

In some implementations, the predetermined time period includes at leastone of an amount of time before the time of the anomaly or an amount oftime after the time of the anomaly. In some embodiments, the methodfurther includes determining, by the device, one or more highlights ofthe service associated with the detected anomaly. In some embodiments,displaying each of the snapshots includes displaying, by the device, theone or more highlights in the service graph displayed via the set ofsnapshots. In some embodiments, the method further includes identifying,by the device, one or more changes to one of a status or metric of theone or more microservices within the predetermined time period of thetime of the anomaly. In some implementations, displaying each of thesnapshots further includes displaying, by the device, the one or morechanges in the service graph displayed via the set of snapshots.

In some implementations, storing the snapshots further includesestablishing, by the device at each of the time increments, metrics forthe plurality of microservices. In some embodiments, the method furtherincludes storing, by the device, metrics at each of the time incrementswith each snapshot of the plurality of snapshots. In someimplementations, detecting the anomaly further includes providing, bythe device responsive to the detection, a notification of the anomaly.In some implementations, the method further includes receiving, by thedevice, a request for the service graph at the time of the incident andresponsive to the request, displaying one or more of the set ofsnapshots.

Another aspect provides a system for displaying a service graph inassociation with a time of a detected anomaly. The system comprises adevice including a plurality of processors, coupled to memory andconfigured to store a plurality of snapshots of a service graph of aplurality of microservices generated for each of a plurality timeincrements over a time period. Each of the plurality of snapshots of theservice graphs include metrics at a respective time increment fromexecution of each of the plurality of microservices. The processors arefurther configured to detect an anomaly with operation of one or moremicroservices of the plurality of services. The processors are furtherconfigured to identify a set of snapshots from the plurality ofsnapshots of the service graph within a predetermined time period of atime of the anomaly. The processors are further configured to displayeach of the snapshots in the set of snapshots in sequence correspondingto time increments within the predetermined time period of the time ofthe anomaly.

In some embodiments, the predetermined time period comprises at leastone of an amount of time before the time of the anomaly or an amount oftime after the time of the anomaly. In some implementations, theprocessor is further configured to determine, by the device, one or morehighlights of the service associated with the detected anomaly. In someembodiments, displaying the set of snapshots includes displaying, by thedevice, the one or more highlights in the service graph displayed viathe set of snapshots. In some implementations, the processor is furtherconfigured to identify, by the device, one or more changes to one of astatus or metric of the one or more microservices within thepredetermined time period of the time of the anomaly. In someembodiments, displaying the set of snapshots includes displaying, by thedevice, the one or more changes in the service graph displayed via theset of snapshots.

In some implementations, storing the snapshots includes establishing, bythe device at each of the time increments, metrics for the plurality ofmicroservices. In some embodiments, the processors are furtherconfigured to store, by the device, metrics at each of the timeincrements with each snapshot of the plurality of snapshots. In someimplementations, detecting the anomaly further includes providing, bythe device responsive to the detection, a notification of the anomaly.In some embodiments, the processors are further configured to receive,by the device, a request for the service graph at the time of theincident and responsive to the request, displaying one or more of theset of snapshots.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

Objects, aspects, features, and advantages of embodiments disclosedherein will become more fully apparent from the following detaileddescription, the appended claims, and the accompanying drawing figuresin which like reference numerals identify similar or identical elements.Reference numerals that are introduced in the specification inassociation with a drawing figure may be repeated in one or moresubsequent figures without additional description in the specificationin order to provide context for other features, and not every elementmay be labeled in every figure. The drawing figures are not necessarilyto scale, emphasis instead being placed upon illustrating embodiments,principles and concepts. The drawings are not intended to limit thescope of the claims included herewith.

FIG. 1A is a block diagram of a network computing system, in accordancewith an illustrative embodiment;

FIG. 1B is a block diagram of a network computing system for deliveringa computing environment from a server to a client via an appliance, inaccordance with an illustrative embodiment;

FIG. 1C is a block diagram of a computing device, in accordance with anillustrative embodiment;

FIG. 2 is a block diagram of an appliance for processing communicationsbetween a client and a server, in accordance with an illustrativeembodiment;

FIG. 3 is a block diagram of a virtualization environment, in accordancewith an illustrative embodiment;

FIG. 4 is a block diagram of a cluster system, in accordance with anillustrative embodiment;

FIG. 5A is a block diagram of a service graph based system, inaccordance with an illustrative embodiment;

FIG. 5B is a block diagram of a service graph, in accordance with anillustrative embodiment;

FIG. 5C is a flow diagram of a method of using a service graph, inaccordance with an illustrative embodiment;

FIG. 6A is an example user interface including a service graph, inaccordance with an illustrative embodiment; and

FIG. 6B is a flow diagram of a method for displaying a service graph inassociation with a time of a detected anomaly, in accordance with anillustrative embodiment.

DETAILED DESCRIPTION

For purposes of reading the description of the various embodimentsbelow, the following descriptions of the sections of the specificationand their respective contents may be helpful:

Section A describes a network environment and computing environmentwhich may be useful for practicing embodiments described herein;

Section B describes embodiments of systems and methods for delivering acomputing environment to a remote user;

Section C describes embodiments of systems and methods for virtualizingan application delivery controller;

Section D describes embodiments of systems and methods for providing aclustered appliance architecture environment; and

Section E describes embodiments of a service graph based platform andtechnology; and

Section F describes embodiments of systems and methods for displaying aservice graph in association with a time of a detected anomaly.

A. Network and Computing Environment

Referring to FIG. 1A, an illustrative network environment 100 isdepicted. Network environment 100 may include one or more clients102(1)-102(n) (also generally referred to as local machine(s) 102 orclient(s) 102) in communication with one or more servers 106(1)-106(n)(also generally referred to as remote machine(s) 106 or server(s) 106)via one or more networks 104(1)-104 n (generally referred to asnetwork(s) 104). In some embodiments, a client 102 may communicate witha server 106 via one or more appliances 200(1)-200 n (generally referredto as appliance(s) 200 or gateway(s) 200).

Although the embodiment shown in FIG. 1A shows one or more networks 104between clients 102 and servers 106, in other embodiments, clients 102and servers 106 may be on the same network 104. The various networks 104may be the same type of network or different types of networks. Forexample, in some embodiments, network 104(1) may be a private networksuch as a local area network (LAN) or a company Intranet, while network104(2) and/or network 104(n) may be a public network, such as a widearea network (WAN) or the Internet. In other embodiments, both network104(1) and network 104(n) may be private networks. Networks 104 mayemploy one or more types of physical networks and/or network topologies,such as wired and/or wireless networks, and may employ one or morecommunication transport protocols, such as transmission control protocol(TCP), internet protocol (IP), user datagram protocol (UDP) or othersimilar protocols.

As shown in FIG. 1A, one or more appliances 200 may be located atvarious points or in various communication paths of network environment100. For example, appliance 200 may be deployed between two networks104(1) and 104(2), and appliances 200 may communicate with one anotherto work in conjunction to, for example, accelerate network trafficbetween clients 102 and servers 106. In other embodiments, the appliance200 may be located on a network 104. For example, appliance 200 may beimplemented as part of one of clients 102 and/or servers 106. In anembodiment, appliance 200 may be implemented as a network device such asCitrix networking (formerly NetScaler®) products sold by Citrix Systems,Inc. of Fort Lauderdale, Fla.

As shown in FIG. 1A, one or more servers 106 may operate as a serverfarm 38. Servers 106 of server farm 38 may be logically grouped, and mayeither be geographically co-located (e.g., on premises) orgeographically dispersed (e.g., cloud based) from clients 102 and/orother servers 106. In an embodiment, server farm 38 executes one or moreapplications on behalf of one or more of clients 102 (e.g., as anapplication server), although other uses are possible, such as a fileserver, gateway server, proxy server, or other similar server uses.Clients 102 may seek access to hosted applications on servers 106.

As shown in FIG. 1A, in some embodiments, appliances 200 may include, bereplaced by, or be in communication with, one or more additionalappliances, such as WAN optimization appliances 205(1)-205(n), referredto generally as WAN optimization appliance(s) 205. For example, WANoptimization appliance 205 may accelerate, cache, compress or otherwiseoptimize or improve performance, operation, flow control, or quality ofservice of network traffic, such as traffic to and/or from a WANconnection, such as optimizing Wide Area File Services (WAFS),accelerating Server Message Block (SMB) or Common Internet File System(CIFS). In some embodiments, appliance 205 may be a performanceenhancing proxy or a WAN optimization controller. In one embodiment,appliance 205 may be implemented as Citrix SD-WAN products sold byCitrix Systems, Inc. of Fort Lauderdale, Fla.

Referring to FIG. 1B, an example network environment, 100′, fordelivering and/or operating a computing network environment on a client102 is shown. As shown in FIG. 1B, a server 106 may include anapplication delivery system 190 for delivering a computing environment,application, and/or data files to one or more clients 102. Client 102may include client agent 120 and computing environment 15. Computingenvironment 15 may execute or operate an application, 16, that accesses,processes or uses a data file 17. Computing environment 15, application16 and/or data file 17 may be delivered via appliance 200 and/or theserver 106.

Appliance 200 may accelerate delivery of all or a portion of computingenvironment 15 to a client 102, for example by the application deliverysystem 190. For example, appliance 200 may accelerate delivery of astreaming application and data file processable by the application froma data center to a remote user location by accelerating transport layertraffic between a client 102 and a server 106. Such acceleration may beprovided by one or more techniques, such as: 1) transport layerconnection pooling, 2) transport layer connection multiplexing, 3)transport control protocol buffering, 4) compression, 5) caching, orother techniques. Appliance 200 may also provide load balancing ofservers 106 to process requests from clients 102, act as a proxy oraccess server to provide access to the one or more servers 106, providesecurity and/or act as a firewall between a client 102 and a server 106,provide Domain Name Service (DNS) resolution, provide one or morevirtual servers or virtual internet protocol servers, and/or provide asecure virtual private network (VPN) connection from a client 102 to aserver 106, such as a secure socket layer (SSL) VPN connection and/orprovide encryption and decryption operations.

Application delivery management system 190 may deliver computingenvironment 15 to a user (e.g., client 102), remote or otherwise, basedon authentication and authorization policies applied by policy engine195. A remote user may obtain a computing environment and access toserver stored applications and data files from any network-connecteddevice (e.g., client 102). For example, appliance 200 may request anapplication and data file from server 106. In response to the request,application delivery system 190 and/or server 106 may deliver theapplication and data file to client 102, for example via an applicationstream to operate in computing environment 15 on client 102, or via aremote-display protocol or otherwise via remote-based or server-basedcomputing. In an embodiment, application delivery system 190 may beimplemented as any portion of the Citrix Workspace Suite™ by CitrixSystems, Inc., such as Citrix Virtual Apps and Desktops (formerlyXenApp® and XenDesktop®).

Policy engine 195 may control and manage the access to, and executionand delivery of, applications. For example, policy engine 195 maydetermine the one or more applications a user or client 102 may accessand/or how the application should be delivered to the user or client102, such as a server-based computing, streaming or delivering theapplication locally to the client 120 for local execution.

For example, in operation, a client 102 may request execution of anapplication (e.g., application 16′) and application delivery system 190of server 106 determines how to execute application 16′, for examplebased upon credentials received from client 102 and a user policyapplied by policy engine 195 associated with the credentials. Forexample, application delivery system 190 may enable client 102 toreceive application-output data generated by execution of theapplication on a server 106, may enable client 102 to execute theapplication locally after receiving the application from server 106, ormay stream the application via network 104 to client 102. For example,in some embodiments, the application may be a server-based or aremote-based application executed on server 106 on behalf of client 102.Server 106 may display output to client 102 using a thin-client orremote-display protocol, such as the Independent Computing Architecture(ICA) protocol by Citrix Systems, Inc. of Fort Lauderdale, Fla. Theapplication may be any application related to real-time datacommunications, such as applications for streaming graphics, streamingvideo and/or audio or other data, delivery of remote desktops orworkspaces or hosted services or applications, for exampleinfrastructure as a service (IaaS), desktop as a service (DaaS),workspace as a service (WaaS), software as a service (SaaS) or platformas a service (PaaS).

One or more of servers 106 may include a performance monitoring serviceor agent 197. In some embodiments, a dedicated one or more servers 106may be employed to perform performance monitoring. Performancemonitoring may be performed using data collection, aggregation,analysis, management and reporting, for example by software, hardware ora combination thereof. Performance monitoring may include one or moreagents for performing monitoring, measurement and data collectionactivities on clients 102 (e.g., client agent 120), servers 106 (e.g.,agent 197) or an appliance 200 and/or 205 (agent not shown). In general,monitoring agents (e.g., 120 and/or 197) execute transparently (e.g., inthe background) to any application and/or user of the device. In someembodiments, monitoring agent 197 includes any of the productembodiments referred to as Citrix Analytics or Citrix ApplicationDelivery Management by Citrix Systems, Inc. of Fort Lauderdale, Fla.

The monitoring agents 120 and 197 may monitor, measure, collect, and/oranalyze data on a predetermined frequency, based upon an occurrence ofgiven event(s), or in real time during operation of network environment100. The monitoring agents may monitor resource consumption and/orperformance of hardware, software, and/or communications resources ofclients 102, networks 104, appliances 200 and/or 205, and/or servers106. For example, network connections such as a transport layerconnection, network latency, bandwidth utilization, end-user responsetimes, application usage and performance, session connections to anapplication, cache usage, memory usage, processor usage, storage usage,database transactions, client and/or server utilization, active users,duration of user activity, application crashes, errors, or hangs, thetime required to log-in to an application, a server, or the applicationdelivery system, and/or other performance conditions and metrics may bemonitored.

The monitoring agents 120 and 197 may provide application performancemanagement for application delivery system 190. For example, based uponone or more monitored performance conditions or metrics, applicationdelivery system 190 may be dynamically adjusted, for exampleperiodically or in real-time, to optimize application delivery byservers 106 to clients 102 based upon network environment performanceand conditions.

In described embodiments, clients 102, servers 106, and appliances 200and 205 may be deployed as and/or executed on any type and form ofcomputing device, such as any desktop computer, laptop computer, ormobile device capable of communication over at least one network andperforming the operations described herein. For example, clients 102,servers 106 and/or appliances 200 and 205 may each correspond to onecomputer, a plurality of computers, or a network of distributedcomputers such as computer 101 shown in FIG. 1C.

As shown in FIG. 1C, computer 101 may include one or more processors103, volatile memory 122 (e.g., RAM), non-volatile memory 128 (e.g., oneor more hard disk drives (HDDs) or other magnetic or optical storagemedia, one or more solid state drives (SSDs) such as a flash drive orother solid state storage media, one or more hybrid magnetic and solidstate drives, and/or one or more virtual storage volumes, such as acloud storage, or a combination of such physical storage volumes andvirtual storage volumes or arrays thereof), user interface (UI) 123, oneor more communications interfaces 118, and communication bus 150. Userinterface 123 may include graphical user interface (GUI) 124 (e.g., atouchscreen, a display, etc.) and one or more input/output (I/O) devices126 (e.g., a mouse, a keyboard, etc.). Non-volatile memory 128 storesoperating system 115, one or more applications 116, and data 117 suchthat, for example, computer instructions of operating system 115 and/orapplications 116 are executed by processor(s) 103 out of volatile memory122. Data may be entered using an input device of GUI 124 or receivedfrom I/O device(s) 126. Various elements of computer 101 may communicatevia communication bus 150. Computer 101 as shown in FIG. 1C is shownmerely as an example, as clients 102, servers 106 and/or appliances 200and 205 may be implemented by any computing or processing environmentand with any type of machine or set of machines that may have suitablehardware and/or software capable of operating as described herein.

Processor(s) 103 may be implemented by one or more programmableprocessors executing one or more computer programs to perform thefunctions of the system. As used herein, the term “processor” describesan electronic circuit that performs a function, an operation, or asequence of operations. The function, operation, or sequence ofoperations may be hard coded into the electronic circuit or soft codedby way of instructions held in a memory device. A “processor” mayperform the function, operation, or sequence of operations using digitalvalues or using analog signals. In some embodiments, the “processor” canbe embodied in one or more application specific integrated circuits(ASICs), microprocessors, digital signal processors, microcontrollers,field programmable gate arrays (FPGAs), programmable logic arrays(PLAs), multi-core processors, or general-purpose computers withassociated memory. The “processor” may be analog, digital ormixed-signal. In some embodiments, the “processor” may be one or morephysical processors or one or more “virtual” (e.g., remotely located or“cloud”) processors.

Communications interfaces 118 may include one or more interfaces toenable computer 101 to access a computer network such as a LAN, a WAN,or the Internet through a variety of wired and/or wireless or cellularconnections.

In described embodiments, a first computing device 101 may execute anapplication on behalf of a user of a client computing device (e.g., aclient 102), may execute a virtual machine, which provides an executionsession within which applications execute on behalf of a user or aclient computing device (e.g., a client 102), such as a hosted desktopsession, may execute a terminal services session to provide a hosteddesktop environment, or may provide access to a computing environmentincluding one or more of: one or more applications, one or more desktopapplications, and one or more desktop sessions in which one or moreapplications may execute.

Additional details of the implementation and operation of networkenvironment 100, clients 102, servers 106, and appliances 200 and 205may be as described in U.S. Pat. No. 9,538,345, issued Jan. 3, 2017 toCitrix Systems, Inc. of Fort Lauderdale, Fla.

B. Appliance Architecture

FIG. 2 shows an example embodiment of appliance 200. As describedherein, appliance 200 may be implemented as a server, gateway, router,switch, bridge or other type of computing or network device. As shown inFIG. 2, an embodiment of appliance 200 may include a hardware layer 206and a software layer 205 divided into a user space 202 and a kernelspace 204. Hardware layer 206 provides the hardware elements upon whichprograms and services within kernel space 204 and user space 202 areexecuted and allow programs and services within kernel space 204 anduser space 202 to communicate data both internally and externally withrespect to appliance 200. As shown in FIG. 2, hardware layer 206 mayinclude one or more processing units 262 for executing software programsand services, memory 264 for storing software and data, network ports266 for transmitting and receiving data over a network, and encryptionprocessor 260 for encrypting and decrypting data such as in relation toSecure Socket Layer (SSL) or Transport Layer Security (TLS) processingof data transmitted and received over the network.

An operating system of appliance 200 allocates, manages, or otherwisesegregates the available system memory into kernel space 204 and userspace 202. Kernel space 204 is reserved for running kernel 230,including any device drivers, kernel extensions or other kernel relatedsoftware. As known to those skilled in the art, kernel 230 is the coreof the operating system, and provides access, control, and management ofresources and hardware-related elements of application 104. Kernel space204 may also include a number of network services or processes workingin conjunction with cache manager 232.

Appliance 200 may include one or more network stacks 267, such as aTCP/IP based stack, for communicating with client(s) 102, server(s) 106,network(s) 104, and/or other appliances 200 or 205. For example,appliance 200 may establish and/or terminate one or more transport layerconnections between clients 102 and servers 106. Each network stack 267may include a buffer 243 for queuing one or more network packets fortransmission by appliance 200.

Kernel space 204 may include cache manager 232, packet engine 240,encryption engine 234, policy engine 236 and compression engine 238. Inother words, one or more of processes 232, 240, 234, 236 and 238 run inthe core address space of the operating system of appliance 200, whichmay reduce the number of data transactions to and from the memory and/orcontext switches between kernel mode and user mode, for example sincedata obtained in kernel mode may not need to be passed or copied to auser process, thread or user level data structure.

Cache manager 232 may duplicate original data stored elsewhere or datapreviously computed, generated or transmitted to reducing the accesstime of the data. In some embodiments, the cache memory may be a dataobject in memory 264 of appliance 200, or may be a physical memoryhaving a faster access time than memory 264.

Policy engine 236 may include a statistical engine or otherconfiguration mechanism to allow a user to identify, specify, define orconfigure a caching policy and access, control and management ofobjects, data or content being cached by appliance 200, and define orconfigure security, network traffic, network access, compression orother functions performed by appliance 200.

Encryption engine 234 may process any security related protocol, such asSSL or TLS. For example, encryption engine 234 may encrypt and decryptnetwork packets, or any portion thereof, communicated via appliance 200,may setup or establish SSL, TLS or other secure connections, for examplebetween client 102, server 106, and/or other appliances 200 or 205. Insome embodiments, encryption engine 234 may use a tunneling protocol toprovide a VPN between a client 102 and a server 106. In someembodiments, encryption engine 234 is in communication with encryptionprocessor 260. Compression engine 238 compresses network packetsbi-directionally between clients 102 and servers 106 and/or between oneor more appliances 200.

Packet engine 240 may manage kernel-level processing of packets receivedand transmitted by appliance 200 via network stacks 267 to send andreceive network packets via network ports 266. Packet engine 240 mayoperate in conjunction with encryption engine 234, cache manager 232,policy engine 236 and compression engine 238, for example to performencryption/decryption, traffic management such as request-level contentswitching and request-level cache redirection, and compression anddecompression of data.

User space 202 is a memory area or portion of the operating system usedby user mode applications or programs otherwise running in user mode. Auser mode application may not access kernel space 204 directly and usesservice calls in order to access kernel services. User space 202 mayinclude graphical user interface (GUI) 210, a command line interface(CLI) 212, shell services 214, health monitor 216, and daemon services218. GUI 210 and CLI 212 enable a system administrator or other user tointeract with and control the operation of appliance 200, such as viathe operating system of appliance 200. Shell services 214 include theprograms, services, tasks, processes or executable instructions tosupport interaction with appliance 200 by a user via the GUI 210 and/orCLI 212.

Health monitor 216 monitors, checks, reports and ensures that networksystems are functioning properly and that users are receiving requestedcontent over a network, for example by monitoring activity of appliance200. In some embodiments, health monitor 216 intercepts and inspects anynetwork traffic passed via appliance 200. For example, health monitor216 may interface with one or more of encryption engine 234, cachemanager 232, policy engine 236, compression engine 238, packet engine240, daemon services 218, and shell services 214 to determine a state,status, operating condition, or health of any portion of the appliance200. Further, health monitor 216 may determine if a program, process,service or task is active and currently running, check status, error orhistory logs provided by any program, process, service or task todetermine any condition, status or error with any portion of appliance200. Additionally, health monitor 216 may measure and monitor theperformance of any application, program, process, service, task orthread executing on appliance 200.

Daemon services 218 are programs that run continuously or in thebackground and handle periodic service requests received by appliance200. In some embodiments, a daemon service may forward the requests toother programs or processes, such as another daemon service 218 asappropriate.

As described herein, appliance 200 may relieve servers 106 of much ofthe processing load caused by repeatedly opening and closing transportlayer connections to clients 102 by opening one or more transport layerconnections with each server 106 and maintaining these connections toallow repeated data accesses by clients via the Internet (e.g.,“connection pooling”). To perform connection pooling, appliance 200 maytranslate or multiplex communications by modifying sequence numbers andacknowledgment numbers at the transport layer protocol level (e.g.,“connection multiplexing”). Appliance 200 may also provide switching orload balancing for communications between the client 102 and server 106.

As described herein, each client 102 may include client agent 120 forestablishing and exchanging communications with appliance 200 and/orserver 106 via a network 104. Client 102 may have installed and/orexecute one or more applications that are in communication with network104. Client agent 120 may intercept network communications from anetwork stack used by the one or more applications. For example, clientagent 120 may intercept a network communication at any point in anetwork stack and redirect the network communication to a destinationdesired, managed or controlled by client agent 120, for example tointercept and redirect a transport layer connection to an IP address andport controlled or managed by client agent 120. Thus, client agent 120may transparently intercept any protocol layer below the transportlayer, such as the network layer, and any protocol layer above thetransport layer, such as the session, presentation or applicationlayers. Client agent 120 can interface with the transport layer tosecure, optimize, accelerate, route or load-balance any communicationsprovided via any protocol carried by the transport layer.

In some embodiments, client agent 120 is implemented as an IndependentComputing Architecture (ICA) client developed by Citrix Systems, Inc. ofFort Lauderdale, Fla. Client agent 120 may perform acceleration,streaming, monitoring, and/or other operations. For example, clientagent 120 may accelerate streaming an application from a server 106 to aclient 102. Client agent 120 may also perform end-pointdetection/scanning and collect end-point information about client 102for appliance 200 and/or server 106. Appliance 200 and/or server 106 mayuse the collected information to determine and provide access,authentication and authorization control of the client's connection tonetwork 104. For example, client agent 120 may identify and determineone or more client-side attributes, such as: the operating system and/ora version of an operating system, a service pack of the operatingsystem, a running service, a running process, a file, presence orversions of various applications of the client, such as antivirus,firewall, security, and/or other software.

Additional details of the implementation and operation of appliance 200may be as described in U.S. Pat. No. 9,538,345, issued Jan. 3, 2017 toCitrix Systems, Inc. of Fort Lauderdale, Fla.

C. Systems and Methods for Providing Virtualized Application DeliveryController

Referring now to FIG. 3, a block diagram of a virtualized environment300 is shown. As shown, a computing device 302 in virtualizedenvironment 300 includes a virtualization layer 303, a hypervisor layer304, and a hardware layer 307. Hypervisor layer 304 includes one or morehypervisors (or virtualization managers) 301 that allocates and managesaccess to a number of physical resources in hardware layer 307 (e.g.,physical processor(s) 321 and physical disk(s) 328) by at least onevirtual machine (VM) (e.g., one of VMs 306) executing in virtualizationlayer 303. Each VM 306 may include allocated virtual resources such asvirtual processors 332 and/or virtual disks 342, as well as virtualresources such as virtual memory and virtual network interfaces. In someembodiments, at least one of VMs 306 may include a control operatingsystem (e.g., 305) in communication with hypervisor 301 and used toexecute applications for managing and configuring other VMs (e.g., guestoperating systems 310) on device 302.

In general, hypervisor(s) 301 may provide virtual resources to anoperating system of VMs 306 in any manner that simulates the operatingsystem having access to a physical device. Thus, hypervisor(s) 301 maybe used to emulate virtual hardware, partition physical hardware,virtualize physical hardware, and execute virtual machines that provideaccess to computing environments. In an illustrative embodiment,hypervisor(s) 301 may be implemented as a Citrix Hypervisor by CitrixSystems, Inc. of Fort Lauderdale, Fla. In an illustrative embodiment,device 302 executing a hypervisor that creates a virtual machineplatform on which guest operating systems may execute is referred to asa host server. 302

Hypervisor 301 may create one or more VMs 306 in which an operatingsystem (e.g., control operating system 305 and/or guest operating system310) executes. For example, the hypervisor 301 loads a virtual machineimage to create VMs 306 to execute an operating system. Hypervisor 301may present VMs 306 with an abstraction of hardware layer 307, and/ormay control how physical capabilities of hardware layer 307 arepresented to VMs 306. For example, hypervisor(s) 301 may manage a poolof resources distributed across multiple physical computing devices.

In some embodiments, one of VMs 306 (e.g., the VM executing controloperating system 305) may manage and configure other of VMs 306, forexample by managing the execution and/or termination of a VM and/ormanaging allocation of virtual resources to a VM. In variousembodiments, VMs may communicate with hypervisor(s) 301 and/or other VMsvia, for example, one or more Application Programming Interfaces (APIs),shared memory, and/or other techniques.

In general, VMs 306 may provide a user of device 302 with access toresources within virtualized computing environment 300, for example, oneor more programs, applications, documents, files, desktop and/orcomputing environments, or other resources. In some embodiments, VMs 306may be implemented as fully virtualized VMs that are not aware that theyare virtual machines (e.g., a Hardware Virtual Machine or HVM). In otherembodiments, the VM may be aware that it is a virtual machine, and/orthe VM may be implemented as a paravirtualized (PV) VM.

Although shown in FIG. 3 as including a single virtualized device 302,virtualized environment 300 may include a plurality of networked devicesin a system in which at least one physical host executes a virtualmachine. A device on which a VM executes may be referred to as aphysical host and/or a host machine. For example, appliance 200 may beadditionally or alternatively implemented in a virtualized environment300 on any computing device, such as a client 102, server 106 orappliance 200. Virtual appliances may provide functionality foravailability, performance, health monitoring, caching and compression,connection multiplexing and pooling and/or security processing (e.g.,firewall, VPN, encryption/decryption, etc.), similarly as described inregard to appliance 200.

Additional details of the implementation and operation of virtualizedcomputing environment 300 may be as described in U.S. Pat. No.9,538,345, issued Jan. 3, 2017 to Citrix Systems, Inc. of FortLauderdale, Fla.

In some embodiments, a server may execute multiple virtual machines 306,for example on various cores of a multi-core processing system and/orvarious processors of a multiple processor device. For example, althoughgenerally shown herein as “processors” (e.g., in FIGS. 1C, 2 and 3), oneor more of the processors may be implemented as either single- ormulti-core processors to provide a multi-threaded, parallel architectureand/or multi-core architecture. Each processor and/or core may have oruse memory that is allocated or assigned for private or local use thatis only accessible by that processor/core, and/or may have or use memorythat is public or shared and accessible by multiple processors/cores.Such architectures may allow work, task, load or network trafficdistribution across one or more processors and/or one or more cores(e.g., by functional parallelism, data parallelism, flow-based dataparallelism, etc.).

Further, instead of (or in addition to) the functionality of the coresbeing implemented in the form of a physical processor/core, suchfunctionality may be implemented in a virtualized environment (e.g.,300) on a client 102, server 106 or appliance 200, such that thefunctionality may be implemented across multiple devices, such as acluster of computing devices, a server farm or network of computingdevices, etc. The various processors/cores may interface or communicatewith each other using a variety of interface techniques, such as core tocore messaging, shared memory, kernel APIs, etc.

In embodiments employing multiple processors and/or multiple processorcores, described embodiments may distribute data packets among cores orprocessors, for example to balance the flows across the cores. Forexample, packet distribution may be based upon determinations offunctions performed by each core, source and destination addresses,and/or whether: a load on the associated core is above a predeterminedthreshold; the load on the associated core is below a predeterminedthreshold; the load on the associated core is less than the load on theother cores; or any other metric that can be used to determine where toforward data packets based in part on the amount of load on a processor.

For example, data packets may be distributed among cores or processesusing receive-side scaling (RSS) in order to process packets usingmultiple processors/cores in a network. RSS generally allows packetprocessing to be balanced across multiple processors/cores whilemaintaining in-order delivery of the packets. In some embodiments, RSSmay use a hashing scheme to determine a core or processor for processinga packet.

The RSS may generate hashes from any type and form of input, such as asequence of values. This sequence of values can include any portion ofthe network packet, such as any header, field or payload of networkpacket, and include any tuples of information associated with a networkpacket or data flow, such as addresses and ports. The hash result or anyportion thereof may be used to identify a processor, core, engine, etc.,for distributing a network packet, for example via a hash table,indirection table, or other mapping technique.

Additional details of the implementation and operation of amulti-processor and/or multi-core system may be as described in U.S.Pat. No. 9,538,345, issued Jan. 3, 2017 to Citrix Systems, Inc. of FortLauderdale, Fla.

D. Systems and Methods for Providing a Distributed Cluster Architecture

Although shown in FIGS. 1A and 1B as being single appliances, appliances200 may be implemented as one or more distributed or clusteredappliances. Individual computing devices or appliances may be referredto as nodes of the cluster. A centralized management system may performload balancing, distribution, configuration, or other tasks to allow thenodes to operate in conjunction as a single computing system. Such acluster may be viewed as a single virtual appliance or computing device.FIG. 4 shows a block diagram of an illustrative computing device clusteror appliance cluster 400. A plurality of appliances 200 or othercomputing devices (e.g., nodes) may be joined into a single cluster 400.Cluster 400 may operate as an application server, network storageserver, backup service, or any other type of computing device to performmany of the functions of appliances 200 and/or 205.

In some embodiments, each appliance 200 of cluster 400 may beimplemented as a multi-processor and/or multi-core appliance, asdescribed herein. Such embodiments may employ a two-tier distributionsystem, with one appliance if the cluster distributing packets to nodesof the cluster, and each node distributing packets for processing toprocessors/cores of the node. In many embodiments, one or more ofappliances 200 of cluster 400 may be physically grouped orgeographically proximate to one another, such as a group of bladeservers or rack mount devices in a given chassis, rack, and/or datacenter. In some embodiments, one or more of appliances 200 of cluster400 may be geographically distributed, with appliances 200 notphysically or geographically co-located. In such embodiments,geographically remote appliances may be joined by a dedicated networkconnection and/or VPN. In geographically distributed embodiments, loadbalancing may also account for communications latency betweengeographically remote appliances.

In some embodiments, cluster 400 may be considered a virtual appliance,grouped via common configuration, management, and purpose, rather thanas a physical group. For example, an appliance cluster may comprise aplurality of virtual machines or processes executed by one or moreservers.

As shown in FIG. 4, appliance cluster 400 may be coupled to a firstnetwork 104(1) via client data plane 402, for example to transfer databetween clients 102 and appliance cluster 400. Client data plane 402 maybe implemented a switch, hub, router, or other similar network deviceinternal or external to cluster 400 to distribute traffic across thenodes of cluster 400. For example, traffic distribution may be performedbased on equal-cost multi-path (ECMP) routing with next hops configuredwith appliances or nodes of the cluster, open-shortest path first(OSPF), stateless hash-based traffic distribution, link aggregation(LAG) protocols, or any other type and form of flow distribution, loadbalancing, and routing.

Appliance cluster 400 may be coupled to a second network 104(2) viaserver data plane 404. Similarly to client data plane 402, server dataplane 404 may be implemented as a switch, hub, router, or other networkdevice that may be internal or external to cluster 400. In someembodiments, client data plane 402 and server data plane 404 may bemerged or combined into a single device.

In some embodiments, each appliance 200 of cluster 400 may be connectedvia an internal communication network or back plane 406. Back plane 406may enable inter-node or inter-appliance control and configurationmessages, for inter-node forwarding of traffic, and/or for communicatingconfiguration and control traffic from an administrator or user tocluster 400. In some embodiments, back plane 406 may be a physicalnetwork, a VPN or tunnel, or a combination thereof.

Additional details of cluster 400 may be as described in U.S. Pat. No.9,538,345, issued Jan. 3, 2017 to Citrix Systems, Inc. of FortLauderdale, Fla.

E. Service Graph Based Platform and Technology

Referring now to FIGS. 5A-5C, implementation of systems and methods fora service graph based platform and technology will be discussed. Aservice graph is a useful technology tool for visualizing a service byits topology of components and network elements. Services may be made upof microservices with each microservice handling a particular set of oneor more functions of the service. Network traffic may traverse theservice topology such as a client communicating with a server to accessservice (e.g., north-south traffic). Network traffic of a service mayinclude network traffic communicated between microservices of theservices such as within a data center or between data centers (e.g.,east-west traffic). The service graph may be used to identify andprovide metrics of such network traffic of the service as well asoperation and performance of any network elements used to provide theservice. Service graphs may be used for identifying and determiningissues with the service and which part of the topology causing theissue. Services graphs may be used to provide for administering,managing and configuring of services to improve operational performanceof such services.

Referring to FIG. 5A, an implementation of a system for service graphs,such as those illustrated in FIG. 5B, will be described. A device on anetwork, such as a network device 200, 205 or a server 206, may includea service graph generator and configurator 512, a service graph display514 and service graph monitor 516. The service graph generator andconfigurator 512 (generally referred to as service graph generator 512),may identify a topology 510 of elements in the network and metrics 518related to the network and the elements, to generate and/or configureservice graphs 505A-N. The service graphs 505A-N (generally referred toas service graphs 505) may be stored in one or more databases 520, withany of the metric 518′ and/or topology 510′. The service graphicgenerator 512 may generate data of the service graphs 505 to bedisplayed in a display or rendered form such as via a user interface,generated referred to as service graph display 514. Service graphmonitor 516 may monitor the network elements of the topology and servicefor metrics 518 to configure and generate a service graph 505 and/or toupdate dynamically or in real-time the elements and metrics 518 of orrepresented by a service graph display 514.

The topology 510 may include data identifying, describing, specifying orotherwise representing any elements used, traversed in accessing any oneor more services or otherwise included with or part of such one or moreservices, such as any of the services 275 described herein. The topologymay include data identifying or describing any one or more networks andnetwork elements traversed to access or use the services, including anynetwork devices, routers, switches, gateways, proxies, appliances,network connections or links, Internet Service Providers (ISPs), etc.The topology may include data identifying or describing any one or moreapplications, software, programs, services, processes, tasks orfunctions that are used or traversed in accessing a service. In someimplementations, a service may be made up or include multiplemicroservices, each providing one or more functions, functionality oroperations of or for a service. The topology may include dataidentifying or describing any one or more components of a service, suchas programs, functions, applications or microservices used to providethe service. The topology may include parameters, configuration dataand/or metadata about any portion of the topology, such as any elementof the topology.

A service graph 505 may include data representing the topology of aservice 275, such any elements making up such a service or used by theservice, for example as illustrated in FIG. 5B. The service graph may bein a node base form, such as graphical form of nodes and each noderepresenting an element or function of the topology of the service. Aservice graph may represent the topology of a service using nodesconnected among each other via various connectors or links, which may bereferred to as arcs. The arc may identify a relationship betweenelements connected by the arc. Nodes and arcs may be arranged in amanner to identify or describe one or more services. Nodes and arcs maybe arranged in a manner to identify or describe functions provided bythe one or more services. For example, a function node may represent afunction that is applied to the traffic, such as a transform (SSLtermination, VPN gateway), filter (firewalls), or terminal (intrusiondetection systems). A function within the service graph might use one ormore parameters and have one or more connectors.

The service graph may include any combination of nodes and arcs torepresent a service, topology or portions thereof. Nodes and arcs may bearranged in a manner to identify or describe the physical and/or logicaldeployment of the service and any elements used to access the service.Nodes and arcs may be arranged in a manner to identify or describe theflow of network traffic in accessing or using a service. Nodes and arcsmay be arranged in a manner to identify or describe the components of aservice, such as multiple microservices that communicate with each otherto provide functionality of the service. The service graph may be storedin storage, such as database 520, in a manner in order for the servicegraph generator to generate a service graph in memory and/or render theservice graph in display form 514.

The service graph generator 512 may include an application, program,library, script, service, process, task or any type and form ofexecutable instructions for establishing, creating, generating,implementing, configuring or updating a service graph 505. The servicegraph generator may read and/or write data representing the servicegraph to a database, file or other type of storage. The service graphgenerator may comprise logic, functions and operations to construct thearrangement of nodes and arcs to have an electronic representation ofthe service graph in memory. The service graph generator may read oraccess the data in the database and store data into data structures andmemory elements to provide or implement a node based representation ofthe service graph that can be updated or modified. The service graphgenerator may use any information from the topology to generate aservice graph. The service graph generator may make network calls or usediscovery protocols to identify the topology or any portions thereof.The service graph generator may use any metrics, such as in memory orstorage or from other devices, to generate a service graph. The servicegraph generator may comprise logic, functions and operations toconstruct the arrangement of nodes and arcs to provide a graphical orvisual representation of the service graph, such as on a user interfaceof a display device. The service graph generator may comprise logic,functions and operations to configure any node or arc of the servicegraph to represent a configuration or parameter of the corresponding orunderlying element represented by the node or arc. The service graphgenerator may comprise logic, functions and operations to include,identify or provide metrics in connection with or as part of thearrangement of nodes and arcs of the service graph display. The servicegraph generator may comprise an application programming interface (API)for programs, applications, services, tasks, processes or systems tocreate, modify or interact with a service graph.

The service graph display 514 may include any graphical or electronicrepresentation of a service graph 505 for rendering or display on anytype and form of display device. The service graph display may berendered in visual form to have any type of color, shape, size or othergraphical indicators of the nodes and arcs of the service graph torepresent a state or status of the respective elements. The servicegraph display may be rendered in visual form to have any type of color,shape, size or other graphical indicators of the nodes and arcs of theservice graph to represent a state or status of one or more metrics. Theservice graph display may comprise any type of user interface, such as adashboard, that provides the visual form of the service graph. Theservice graph display may include any type and form of user interfaceelements to allow users to interact, interface or manipulate a servicegraph. Portion of the service graph display may be selectable toidentify information, such as metrics or topology information about thatportion of the service graph. Portions of the service graph display mayprovide user interface elements for users to take an action with respectto the service graph or portion thereof, such as to modify aconfiguration or parameter of the element.

The service graph monitor 518 may include an application, program,library, script, service, process, task or any type and form ofexecutable instructions to receive, identify, process metrics 518 of thetopology 510. The service graph monitor 518 monitors via metrics 518 theconfiguration, performance and operation of elements of a service graph.The service graph monitor may obtain metrics from one or more devices onthe network. The service graph monitor may identify or generate metricsfrom network traffic traversing the device(s) of the service graphmonitor. The service graph monitor may receive reports of metrics fromany of the elements of the topology, such as any elements represented bya node in the service graph. The service graph monitor may receivereports of metrics from the service. From the metrics, the service graphmonitor may determine the state, status or condition of an elementrepresented in or by the service graph, such as by a node of the servicegraph. From the metrics, the service graph monitor may determine thestate, status or condition of network traffic or network connectedrepresented in or by the service graph, such as by an arc of the servicegraph. The service graph generator and/or service graph monitor mayupdate the service graph display, such as continuously or inpredetermined frequencies or event based, with any metrics or anychanged in the state, status or condition of a node or arc, elementrepresented by the node or arc, the service, network or network traffictraversing the topology.

The metrics 518, 518′ (generally referred to as metrics 518) may bestored on network device in FIG. 5B, such as in memory or storage. Themetrics 518, 518′ may be stored in a database, such as database 520 inFIG. 5A, on the same device or over a network to another device, such asa server. Metrics may include any type and form of measurement of anyelement of the topology, service or network. Metrics may include metricson volume, rate or timing of requests or responses received, transmittedor traversing the network element represented by the node or arc. AMetrics may include metrics on usage of a resource by the elementrepresented by the node or arc, such as memory, bandwidth. Metrics mayinclude metrics on performance and operation of a service, including anycomponents or microservices of the service, such as rate of response,transaction responses and times.

FIG. 5B illustrates an implementation of a service graph in connectionwith micro-services of a service in view of east-west network trafficand north-south network traffic. In brief overview, clients 102 mayaccess via one or more networks 104 a data center having servers106A-106N (generally referred to as servers 106) providing one or moreservices 275A-275N (generally referred to as services 275). The servicesmay be made up multiple microservices 575A-575N (generally referred toas microservice or micro service 575). Service 275A may includemicroservice 575A and 575N while service 275B may include microservice575B and 575N. The microservices may communicate among the microservicesvia application programming interface (APIs). A service graph 505 mayrepresent a topology of the services and metrics on network traffic,such as east-west network traffic and north-south network traffic.

North-south network traffic generally describes and is related tonetwork traffic between clients and servers, such as client via networks104 to servers of data center and/or servers to clients via network 104as shown in FIG. 5B. East-west network traffic generally describes andis related to network traffic between elements in the data centers, suchas data center to data center, server to server, service to service ormicroservice to microservice.

A service 275 may comprise microservices 575. In some aspects,microservices is a form of service-oriented architecture style whereinapplications are built as a collection of different smaller servicesrather than one whole or singular application (referred to sometimes asa monolithic application). Instead of a monolithic application, aservice has several independent applications or services (e.g.,microservices) that can run on their own and may be created usingdifferent coding or programming languages. As such, a larger server canbe made up of simpler and independent programs or services that areexecutable by themselves. These smaller programs or services are groupedtogether to deliver the functionalities of the larger service. In someaspects, a microservices based service structures an application as acollection of services that may be loosely coupled. The benefit ofdecomposing a service into different smaller services is that itimproves modularity. This makes the application or service easier tounderstand, develop, test, and be resilient to changes in architectureor deployment.

A microservice includes an implementation of one or more functions orfunctionality. A microservice may be a self-contained piece of businessfunction(s) with clear or established interfaces, such as an applicationprogramming interface (API). In some implementations, a microservice maybe deployed in a virtual machine or a container. A service may use oneor more functions on one microservice and another one or more functionsof a different microservice. In operating or executing a service, onemicroservice may make API calls to another microservice and themicroservice may provide a response via an API call, event handler orother interface mechanism. In operating or executing a microservice, themicroservice may make an API call to another microservice, which in itsoperation or execution, makes a call to another microservice, and so on.

The service graph 505 may include multiple nodes 570A-N connected orlinked via one or more or arcs 572A-572N. The service graph may havedifferent types of nodes. A node type may be used to represent aphysical network element, such as a server, client, appliance or networkdevice. A node type may be used to represent an end point, such as aclient or server. A node type may be used to represent an end pointgroup, such as group of clients or servers. A node type may be used torepresent a logical network element, such as a type of technology,software or service or a grouping or sub-grouping of elements. A nodetype may be used to represent a functional element, such asfunctionality to be provided by an element of the topology or by theservice.

The configuration and/or representation of any of the nodes 570 mayidentify a state, a status and/or metric(s) of the element representedby the node. Graphical features of the node may identify or specify anoperational or performance characteristic of the element represented bythe node. A size, color or shape of the node may identify an operationalstate of whether the element is operational or active. A size, color orshape of the node may identify an error condition or issue with anelement. A size, color or shape of the node may identify a level ofvolume of network traffic, a volume of request or responses received,transmitted or traversing the network element represented by the node. Asize, color or shape of the node may identify a level of usage of aresource by the element represented by the node, such as memory,bandwidth, CPU or storage. A size, color or shape of the node mayidentify relativeness with respect to a threshold for any metricassociated with the node or the element represented by the node.

The configuration and/or representation of any of the arcs 572 mayidentify a state, status and/or metric(s) of the element represented bythe arc. Graphical features of the arc may identify or specify anoperational or performance characteristic of the element represented bythe arc. A size, color or shape of the node may identify an operationalstate of whether the network connection represented by the arc isoperational or active. A size, color or shape of the arc may identify anerror condition or issue with a connection associated with the arc. Asize, color or shape of the arc may identify an error condition or issuewith network traffic associated with the arc. A size, color or shape ofthe arc may identify a level of volume of network traffic, a volume ofrequest or responses received, transmitted or traversing the networkconnection or link represented by the arc. A size, color or shape of thearc may identify a level of usage of a resource by network connection ortraffic represented by the arc, such as bandwidth. A size, color orshape of the node may identify relativeness with respect to a thresholdfor any metric associated with the arc. In some implementations, ametric for the arc may include any measurement of traffic volume perarc, latency per arc or error rate per arc.

Referring now to FIG. 5C, an implementation of a method for generatingand displaying a service graph will be described. In brief overview ofmethod 580, at step 582, a topology is identified, such as for aconfiguration of one or more services. At step 584, the metrics ofelements of the topology, such as for a service are monitored. At step586, a service graph is generated and configured. At step 588, a servicegraph is displayed. At step 590, issues with configuration, operationand performance of a service or the topology may be identified ordetermined.

At step 582, a device identifies a topology for one or more services.The device may obtain, access or receive the topology 510 from storage,such as a database. The device may be configured with a topology for aservice, such as by a user. The device may discover the topology orportions therefore via one more discovery protocols communicated overthe network. The device may obtain or receive the topology or portionsthereof from one or more other devices via the network. The device mayidentify the network elements making up one or more services. The devicemay identify functions providing the one or more services. The devicemay identify other devices or network elements providing the functions.The device may identify the network elements for north-west traffic. Thedevice may identify the network elements for east-west traffic. Thedevice may identify the microservices providing a service. In someimplementations, the service graph generator establishes or generates aservice graph based on the topology. The service graph may be stored tomemory or storage.

At step 584, the metrics of elements of the topology, such as for aservice are monitored. The device may receive metrics about the one ormore network elements of the topology from other devices. The device maydetermine metrics from network traffic traversing the device. The devicemay receive metrics from network elements of the topology, such as viareports or events. The device may monitor the service to obtain orreceive metrics about the service. The metrics may be stored in memoryor storage, such as in association with a corresponding service graph.The device may associate one or more of the metrics with a correspondingnode of a service graph. The device may associate one or more of themetrics with a corresponding arc of a service graph. The device maymonitor and/or obtain and/or receive metrics on a scheduled orpredetermined frequency. The device may monitor and/or obtain and/orreceive metrics on a continuous basis, such as in real-time ordynamically when metrics change.

At step 586, a service graph is generated and configured. A servicegraph generator may generate a service graph based at least on thetopology. A service graph generator may generate a service graph basedat least on a service. A service graph generator may generate a servicegraph based on multiple services. A service graph generator may generatea service graph based at least on the microservices making up a service.A service graph generator may generate a service graph based on a datacenter, servers of the data center and/or services of the data center. Aservice graph generator may generate a service graph based at least oneast-west traffic and corresponding network elements. A service graphgenerator may generate a service graph based at least on north-southtraffic and corresponding network elements. A service graph generatormay configure the service graph with parameters, configuration data ormeta-data about the elements represented by a node or arc of the servicegraph. The service graph may be generated automatically by the device.The service graph may be generated responsive to a request by a user,such as via a comment to or user interface of the device.

At step 588, a service graph is displayed. The device, such as viaservice graph generator, may create a service graph display 514 to bedisplayed or rendered via a display device, such as presented on a userinterface. The service graph display may include visual indicators orgraphical characteristics (e.g., size, shape or color) of the nodes andarcs of the service graph to identify status, state or condition ofelements associated with or corresponding to a node or arc. The servicegraph display may be displayed or presented via a dashboard or otheruser interface in which a user may monitor the status of the service andtopology. The service graph display may be updated to show changes inmetrics or the status, state and/or condition of the service, thetopology or any elements thereof. Via the service graph display, a usermay interface or interact with the service graph to discoverinformation, data and details about any of the network elements, such asthe metrics of a microservice of a service.

At step 590, issues with configuration, operation and performance of aservice or the topology may be identified or determined. The device maydetermine issues with the configuration, operation or performance of aservice by comparing metrics of the service to thresholds. The devicemay determine issues with the configuration, operation or performance ofa service by comparing metrics of the service to previous or historicalvalues. The device may determine issues with the configuration,operation or performance of a service by identifying a change in ametric. The device may determine issues with the configuration,operation or performance of a service by identifying a change in astatus, state or condition of a node or arc or elements represented bythe node or arc. The device may change the configuration and/orparameters of the service graph. The device may change the configurationof the service. The device may change the configuration of the topology.The device may change the configuration of network elements making upthe topology or the service. A user may determine issues with theconfiguration, operation or performance of a service by reviewing,exploring or interacting with the service graph display and any metrics.The user may change the configuration and/or parameters of the servicegraph. The user may change the configuration of the service. The usermay change the configuration of the topology. The device may change theconfiguration of network elements making up the topology or the service.

F. Systems and Methods for Displaying a Service Graph in Associationwith a Time of a Detected Anomaly

Systems and methods for displaying a service graph in association with atime of a detected anomaly are discussed herein. As described above, aservice graph may be a tool by which a service including variousmicroservices corresponding thereto may be visualized. Such a tool maybe used for network traffic monitoring purposes, diagnostic purposes,troubleshooting purposes, and so forth. The service graph may depictvarious traffic volume, latency, error rates, and other metricscorresponding to the service. In some implementations, one or moremicroservices of the service may malfunction, fail, glitch, or otherwiseexperience a condition which is an anomaly. To correct such conditions,an administrator may identify a cause of the condition. However,identification of the cause of the condition may be difficult withoutunderstanding conditions of the microservice(s) leading up to and/orfollowing occurrence of the anomaly.

In some implementations, a device (such as an intermediary device) maystore a plurality of snapshots of a service graph of a plurality ofmicroservices generated for each of a plurality of time increments overa time period. The snapshots may include metrics at a respective timeincrement from execution of each of the microservices. The device maydetect an anomaly with operation of one or more of the microservices.The device may identify a set of snapshots of the service graph within apredetermined time period of a time of the anomaly. The device maydisplay each of a sequence of the snapshots in the set corresponding totime increments within the predetermined time period of the time of theanomaly. In some implementations, the device may display a number ofsnapshots (or snapshots within the predetermined time period) beforeand/or after the time of the anomaly. In some embodiments, the number ofsnapshots (or predetermined time period) may change based on a scale ofthe anomaly. For instance, where the anomaly is a minor anomaly (e.g., asmall change from historical data, as one example) the number ofsnapshots (or predetermined time period) may be smaller than where theanomaly is a greater anomaly (e.g., a large change from historicaldata).

Rather than a user (such as an administrator) manually evaluating themicroservice(s) which contributed to or caused the anomaly, theimplementations described herein may automatically detect the anomalyand provide a sequence of snapshots of the service graph to identify thestate of the service graph that may have contributed to the anomaly. Theadministrator can thus quickly and efficiently determine the cause ofthe anomaly based on the data from or represented in the service graph.The implementations described herein may increase the efficiency ofdiagnostics of anomalies for microservices corresponding to a service byproviding a visual aid by which an administrator can observe metricscorresponding to the microservice(s) prior to, during, and/or after theanomaly. The implementations described herein may decrease downtime as aresult of such anomalies by providing a faster mechanism by which anadministrator can remediate the cause of the anomaly. Various otherbenefits and advantages of the embodiments described herein are furtherdetailed below.

Referring now to FIG. 6A, depicted is an example user interface 600including a service graph 505. The service graph 505 may be similar tothe service graph 505 shown in FIG. 5B and described above. The userinterface 600 may be configured to display a sequence, order, or seriesof service graphs 505 over a period of time. The service graphs 505 maydepict various metrics. For instance, each service graph 505 in theseries of service graphs 505 may include metrics corresponding to theparticular time at which the service graph 505 was generated, produced,configured, displayed, etc. (e.g., by the service graph generator andconfigurator 512 as described above). The user interface 600 may includea timestamp 605 corresponding to the time at which the service graph 505was generated, produced, configured, displayed, etc. The user interface600 may include a plurality of user interface elements 610 to controlthe displaying or viewing of (e.g., to play, pause, stop, fast forward,rewind, etc.) the series of service graphs 505 within the user interface600. A user, such as an administrator, may provide inputs to the userinterface 600 (e.g., to the user interface elements 610 of the userinterface 600) to determine a cause of an anomaly corresponding to themicroservice(s) as described in greater detail below.

Referring now to FIG. 5A and FIG. 6A, as described in greater detailabove in Section E, the service graph 505 includes any combination ofnodes 570A-N and arcs 572A-N that represent a service, topology, orportions thereof. Nodes 570A-N and arcs 572A-N may be arranged toidentify or describe the physical and/or logical deployment of theservice (e.g., including microservices corresponding to the service),identify or describe the flow of network traffic during access or use ofthe service, and/or any elements used to access the service. The servicegraph generator 512 may read and/or write data representing the servicegraph 505 to a database 520 for subsequent use or display, as describedin greater detail below. The service graph monitor 518 may be configuredto receive, identify, process metrics 518 of the topology 510corresponding to the service graph 505. The service graph monitor 518monitors via metrics 518 the configuration, performance and operation ofelements of a service graph. The service graph monitor 518 may obtainmetrics from one or more devices on the network. The service graphmonitor may identify or generate metrics, such as network traffic rateor flow, latency, error rate, etc. from network traffic traversing thedevice(s) of the service graph monitor.

The service graph generator 512 may generate service graphs 505 atvarious time increments. For instance, the service graph generator 512may generate a service graph 505 for a service at a time interval ofonce every 10 seconds, 20 seconds, 30 seconds, minute, 5 minutes, and soforth. Each service graph 505 may include metrics corresponding to thetraffic at the time of generation by the service graph generator 512,which may be displayed in a window within the user interface 600including the service graph 505, reflected in characteristics of theservice graph 505 itself, and so forth.

The service graph generator 512 may generate store snapshots of each ofthe service graphs 505, such as at time increments while monitoring thetopology corresponding to the service graph. In some embodiments, asnapshot may comprise data to represent, an image of the service graphs505 at a particular time, such as data for generating, constructing,building, assembling, or otherwise forming the service graphs 505 at aparticular time. In some cases, the data may include computer-readableinstructions for generating, constructing, building, assembling, orotherwise forming the service graphs. A snapshot may comprise data forforming the service graphs as well as metrics for including in ordisplaying as part of or with the service graph. In someimplementations, the service graph generator 512 may store snapshots ofthe service graphs 505 in the database 520 in association with thetimestamp corresponding to the time at which the respective servicegraph 505 was generated, displayed, stored, etc. The service graphgenerator 512 may store the metrics for each service graph 505 thedatabase 520 in association with the service graph 505. The metricsand/or service graphs 505 may be stored in the database 520 and may beindexed by the timestamp such that, at any given time, the service graphgenerator 512 can identify a service graph and corresponding metrics forre-generating, re-constructing, re-building, re-assembling, or otherwisere-forming the service graphs 505 based on the data from the database520.

The service graph generator 512 may be configured to generate and storesnapshots of the service graphs 505 and corresponding information(collectively referred to as service graphs 505) for a predeterminedamount of time in the database 520. For instance, the service graphgenerator 512 may store the service graphs 505 in the database 520 for anumber of days, weeks, months, etc. The service graph generator 512and/or a service, program, etc. may be executing on the server hostingthe database 520 and may monitor a difference between the timestampcorresponding to when the service graph 505 is stored or otherwise savedto the database 520 and a current date and/or time. The service graphgenerator 512 and/or service or program may automatically purge the datacorresponding to the service graphs 505 from the database 520. In someimplementations, the service graph generator 512 may maintain apredetermined number of entries in the database 520 which aresequentially written and re-written. As such, the service graphgenerator 512 may overwrite an entry in the database 520 with a newentry corresponding to a service graph 505. In some implementations, theservice graph generator 512 may store the service graphs 505 for a userconfigured period of time.

The service graph monitor 516 may be configured to detect issues withconfiguration, operation, status, and/or performance (collectivelyreferred to herein as anomalies) of execution, implementation, orotherwise operation of a service or microservice within a service or anycomponents of the topology supporting the services and microservices.The service graph monitor 516 may identify anomalies for service ormicroservice by comparing metrics of the service or microservice tothresholds, or previous, historical or benchmark values for suchmetrics. The service graph monitor 516 may monitor metrics (e.g.,network traffic rates, error rates, latency, etc.) of the service ormicroservice continuously, near-continuously, at the time at which theservice graph 505 is generated, etc. The service graph monitor 516 mayinclude, maintain, retrieve, or otherwise access metric thresholdscorresponding the metrics for the service. The metric thresholds may bestatic, dynamic, adaptive, manually or automatically updated, etc. Theservice graph monitor 516 may compare the metrics for the service graph505 to the corresponding metric thresholds. The service graph monitor516 may identify anomalies based on the comparison (e.g., an anomalywith network traffic rate where traffic rate is below a correspondingmetric threshold, an anomaly with error rates where the error rateexceeds a corresponding metric threshold, an anomaly in latency wherelatency exceeds a corresponding metric threshold, and so forth).

The service graph monitor 516 may determine issues with theconfiguration, operation or performance of a service or microservice bycomparing metrics of the service to previous or historical values whichmay be included, retrieved, or otherwise accessed by the service graphmonitor 516, such as by comparing snapshots of a service graph atdifferent points in time. The service graph monitor 516 may determinewhether current metrics are within, for instance, a standard deviationof the previous or historical values, a historical average or mean, etc.The service graph monitor 516 may determine issues with theconfiguration, operation or performance of a service or microservice byidentifying a change or delta in a metric (e.g., a spike in error rates,an increase in latency, a decrease in network traffic including but notlimited to decreases of network traffic to zero network traffic). Theservice graph monitor 516 may determine issues with the configuration,operation or performance of a service or microservice by determining achange in status of the service or microservice (e.g., a change fromactive to inactive, a change from active to partially active, etc.). Theservice graph monitor 516 may monitor the status of each of theservice(s) or microservice(s) which make up the service. The servicesand/or microservices may automatically change their status uponoccurrence of an anomaly (e.g., when network traffic falls below athreshold, when error rates exceed a threshold, latency exceeds athreshold, etc.). The service graph monitor 516 may identify the anomalyupon detection of a status change of the service and/or microservice.

The service graph display 514 may be configured to locate, look-up, or,retrieve, or otherwise identify a set of snapshots of service graphs505. The service graph display 514 may identify the set of snapshots ofthe service graphs 505 based on the time at which the service graphmonitor 516 identifies the anomaly. The service graph monitor 516 mayrecord or otherwise store the time at which the service graph monitor516 identifies the anomaly. The service graph display 514 maycross-reference the time at which the anomaly is detected by the servicegraph monitor 516 with timestamps associated with service graphs 505 inthe database 52. As stated above, the database may be indexed bytimestamp. The service graph display 514 may identify the timestampcorresponding to a service graph 505 closest to the time at which theanomaly is detected by the service graph monitor 516. The service graphdisplay 514 may identify the snapshot of the service graph 505 whichcorresponds to the identified timestamp in the database 520.

The service graph display 514 may be designed or implemented to identifya set of snapshots of the service graph 505 within a predetermined timeperiod from the time of the anomaly. In some embodiments, the servicegraph display 514 may select the predetermined time frame based on theanomaly. For instance, the service graph display 514 may identify ascale of the anomaly based on the comparison of the anomaly to athreshold, based on the comparison of the anomaly to historical metrics,and so forth. The service graph display 514 may select the predeterminedtime period in accordance with the scale of the anomaly. For instance,as the anomaly increases in scale (e.g., a larger difference fromhistorical metrics, for example) the predetermined time period maycorrespondingly increase. Hence, the predetermined time period maychange in proportion to the scale of the anomaly.

The predetermined time period may be configurable by a user. Thepredetermined time period may be a predetermined time before theanomaly. The predetermined time period may be a predetermined time afterthe anomaly. The predetermined time period may be a predetermined timebefore and/after the anomaly, such as a time window in which the anomalyis detected. The predetermined time period may be a predetermined numberof instances of snapshots before the anomaly. The predetermined timeperiod may be a predetermined number of instances of snapshots after theanomaly. The predetermined time period may be a predetermined number ofinstances of snapshots before and/after the anomaly. The predeterminedtime period may be a rolling window of time or instances of servicegraphs in which the anomaly is detected.

After the service graph display 514 identifies the service graph 505having an associated timestamp corresponding to the time at which theservice graph monitor 516 identifies the anomaly, the service graphdisplay 514 may identify one or more additional service graphs 505having timestamps that lead up to and/or follow the time of the anomaly.The corresponding time may be the time closest to the time of theanomaly, either before or after the anomaly or at the time of theanomaly. The corresponding time may be the time at which a snapshot didnot have an anomaly before the time a snapshot has the anomaly. Thepredetermined timeframe may thus include a time prior to the anomaly(e.g., leading up to the anomaly), a time of the anomaly (e.g., closestto the time of the anomaly), and/or a time following the anomaly (e.g.,immediately after the anomaly). The service graph display 514 mayselect, from the database 520, the set of snapshots of the service graph505 within the predetermined timeframe of the time of the anomaly. Insome implementations, the service graph display 514 may identifytimestamps having time which falls within the predetermined timeframe ofthe time of the anomaly. In some implementations (such as where thedatabase 520 is arranged, sorted, or otherwise indexed by timestamp),the service graph display 514 may select a number of entries of thedatabase 520 which precede or follow the entry corresponding to thesnapshot of the service graph 505 having a timestamp closest to the timeof the anomaly.

In some implementations, the service graph display 514 may be configuredto callout, alert, or otherwise highlight the anomaly within the servicegraph 505 within the set of service graphs 505. The service graphdisplay 514 may be configured to highlight the anomaly by highlightingthe metrics which were used by the service graph monitor 516 foridentifying the anomaly. For instance, where the service graph monitor516 identifies the anomaly due to a decrease in network traffic, theservice graph display 514 may highlight the metric corresponding to thenetwork traffic represented on the service graph 505. As anotherexample, where the service graph monitor 516 identifies the anomaly dueto an increase in latency, the service graph display 514 may highlightthe metric corresponding to the latency represented on the service graph505. As yet another example, where the service graph monitor 516identifies the anomaly due to an increase in error rate, the servicegraph display 514 may highlight the metric corresponding to the errorrate represented on the service graph 505.

The service graph display 514 may highlight the metric used foridentifying the anomaly by changing a color of the text in the windowwithin the user interface 600, by increasing the size or prominence ofthe metric within the service graph 505, increasing the size of acorresponding node 575 of the service graph 505, modifying the arcextending between nodes 575, and so forth. In each of these embodiments,the service graph display 514 may be configured to modify the servicegraph(s) 505 within the set to highlight at least a portion or an aspectof the service graphs 505 corresponding to the anomaly.

The service graph display 514 may be designed or implemented to displayeach of the selected snapshots of the service graph 505 in the set insequence of the timestamp. The service graph display 514 may display theset of snapshots by generating a signal which is communicated to aclient device associated with an administrator. The signal may includethe set of snapshots, the sequence, the user interface, etc. (orinstructions for generating the set of snapshots, the user interface,and so forth). The signal may cause the client device to automaticallydisplay each of the snapshots in the set in temporal sequencecorresponding to the service. In some implementations, the signal maycause a notification of the anomaly to display on the client device. Thenotification may prompt a user, such as the administrator, to launch theuser interface 600 to display the sequence of selected snapshots in theset. In some implementations, the service graph display 514 may displayeach of the selected snapshots of the service graph 505 responsive to arequest. The user may generate a request by selecting the user interfaceelement 605 corresponding to playing the sequence of service graphs 505within the set, for instance. The service graph display 514 may receivethe request and display each of the selected snapshots of the servicegraphs 505 responsive to receiving the request. By displaying each ofthe snapshots in order forms a sequence of service graphs similar to orequivalent to a video of service graphs.

Responsive to displaying the sequence of the service graphs 505, a usercan diagnose the cause of the anomaly for modifying the service and/ormicroservices. User can view portions of the sequence of the servicegraph to view a change in any components of the service graph before, ator after the anomaly. The user can select the user interface elements605 to rewind, pause, fast forward, etc. the sequence of service graphs505 within the set to identify the changes in the network traffic whichcaused the anomaly, much as a user would with a video. The servicegraphs 505 may highlight the network conditions which triggered theservice graph monitor 516 to identify the anomaly. The user can view thesequence of service graphs 505 to determine what caused the anomalybased on the network traffic over time. The user can correct the serviceand/or microservices (e.g., by replacing the service and/or microservicewith a previous version, by updating the service and/or microservicewith a new version, and so forth).

Referring now to FIG. 6B, an implementation of a method for displaying aservice graph in association with a time of a detected anomaly will bedescribed. In brief overview of method 615, at step 620, networktraffic, such as network traffic rate, latency, error rates, etc. aremonitored. At step 625, snapshots are stored. At step 630, anomalies mayor may not be detected. At step 635, a set of snapshots are identified.At step 640, the snapshots from the set are displayed.

At step 620, operation, performance and network traffic of the topologysupporting the microservices and microservices, such as network trafficrate, latency, error rates, etc. (e.g., network conditions) aremonitored. In some implementations, a device, such as a network orintermediary device, a server, etc., may determine, identify, monitor,or otherwise establish metrics for the microservices at time increments,such as time increments corresponding to the predetermined time period.The device may monitor metrics about the one or more network elements ofthe topology from other devices. The device may determine metrics fromnetwork traffic traversing the device. The device may monitorperformance and/or status of network traffic between microservices. Thedevice may determine metrics from network traffic traversing the device.The device may receive metrics from network elements of the topology,such as via reports or events. The device may monitor the service toobtain or receive metrics about the service. The device may monitorperformance continuously or at various time increments or intervals. Insome implementations, step 602 may be similar in some aspects to step582 of FIG. 5C.

In some implementations, the device ma generate and store theestablished metrics at each of the time increments. The device may storethe established metrics in a database. The device may store theestablished metrics at each time increment in association with atimestamp corresponding to a current time. The database may store themetrics, the timestamp, and data corresponding to a service graph, asdescribed in greater detail below. The data and metrics may identify theoperation, performance and status of the microservices including anynetwork links between them.

The device may generate a service graph. The device may generate aservice graph with the metrics. The device may generate a service graphand associate the metrics with the elements of the service graph. Insome implementations, a service graph generator of the device maygenerate a service graph. The service graph may model, map, or otherwiserepresent a service including the microservices corresponding thereto.Each service may include a plurality of microservices which performvarious functions which collectively provide the service. The servicegraph may represent a topology of the service or services. A servicegraph generator may generate a service graph based on a data center,servers of the data center and/or services of the data center. Theservice graph may be generated automatically by the device. The servicegraph may be generated responsive to a request by a user, such as via acomment to a user interface of the device. In some implementations, thedevice may generate a service graph similar to the generation of theservice graph at step 586 of FIG. 5C.

At step 625, snapshots (e.g., of service graphs at various timeincrements) are generated and stored. The device may store a pluralityof snapshots of a service graph including a plurality of microservicesfor various time increments over a time period. Each of the snapshots ofthe service graphs may include metrics at the respective time incrementsfrom execution of the microservice(s) of the service. The device maygenerate and store the snapshots at each time increment (such as every10 seconds, 20 seconds, 30 seconds, minute, 5 minutes, and so forth).The device may store the service graphs generated as described above ateach of the time increments. The device may store the service graphsresponsive to occurrence of each of the time increments. The device maystore the service graphs in the database in which the metrics arestored. The device may store the service graphs in association withrespective metrics corresponding thereto. Each entry of the database mayinclude the snapshot corresponding to the service graph, the associatedmetrics, and a timestamp.

At step 630, the device may monitor the topology, microservices orelements of the service graph for any anomalies. Anomalies may or maynot be detected. The device may detect an anomaly with operation of oneor more microservices of the plurality of services. The device maydetect an anomaly with operation of elements of the topology. In someimplementations, a service graph monitor may detect an anomaly based onthe metrics monitored at step 620. The service graph monitor may detectthe anomaly responsive to changes in metrics of the service and/ormicroservices. The service graph monitor may detect the anomaly based ondifferences between monitored metrics and stored thresholds. The servicegraph monitor may detect the anomaly based on differences between themonitored metrics and previous or historical metrics (e.g., outside of athreshold, outside of a standard deviation within the previous orhistorical metrics, from an average or mean of the previous orhistorical metrics, and so forth).

In some embodiments, the device may identify one or more changes to oneof a status, state or condition of the one or more microservices withinthe predetermined time period of the time of the anomaly. Themicroservice(s) may automatically trigger a change in status, state orcondition (e.g., from enabled to disabled, active to inactive, etc.)responsive to the anomaly. The device may monitor the status, state orcondition of each of the microservices. The device may automaticallyidentify change(s) to the status, state or condition of themicroservices upon occurrence of the same.

Where the device does not detect any anomalies, the method 615 mayproceed back to step 625. As such, the device may monitor performance,operation of microservices and network traffic to and via the same untilthe device detects an anomaly. Where the device detects an anomaly, themethod 615 may proceed to step 635. In some implementations, the devicemay continuously monitor for anomalies, such as for any subsequent oradditional anomalies or to provide for status or state of the topology,network elements and microservices of the service graph following theanomaly.

At step 635, where anomalies are detected, a set of snapshots areidentified, such as to form a sequence of snapshots. The device mayidentify a set of snapshots from the plurality of snapshots of theservice graph within a predetermined time period of a time of (e.g.,before and/or after) the anomaly. The device may identify a time atwhich the anomaly is detected (e.g., at step 630). The databaseincluding the snapshots and metrics may be indexed by the timestamps.The device may identify a set of snapshots from the database bycross-referencing the time at with the anomaly is detected with thetimestamps of the databases. The device may identify a first snapshotbased on which snapshot has a timestamp closest to the time at which theanomaly is detected. The device may identify a plurality of othersnapshots for including in the set of snapshots. The device may identifythe other snapshots based on the timestamps within an amount of timebefore the anomaly and/or after the time of the anomaly. In someimplementations, the device may identify a number of snapshots beforeand/or after the time of the anomaly. In some implementations, thedevice may identify each snapshot within a predetermined time periodbefore and/or after the occurrence of the anomaly. In some embodiments,the number of snapshots (or predetermined time period) may change basedon a scale of the anomaly. For instance, where the anomaly is a minoranomaly (e.g., a small change from historical data, as one example) thenumber of snapshots (or predetermined time period) may be smaller thanwhere the anomaly is a greater anomaly (e.g., a large change fromhistorical data). The device may identify the other snapshots to includein the set based on the timestamps which follow or proceed the timestampof the first snapshot.

In some implementations, the device may determine one or more highlightsof the service associated with the detected anomaly. The device maydetermine one or more highlights to highlight the metrics which wereused to determine, identify, or otherwise detect the anomaly. The devicemay determine the highlight(s) responsive to detecting the anomaly. Thedevice may determine the highlight(s) responsive to identifying the setof snapshots. The device may determine the highlight(s) responsive toidentifying the first snapshot. The device may determine thehighlight(s) based on which of the metrics were used by the servicegraph monitor to detect the anomaly (e.g., at step 630). The device maydetermine the highlight(s) by modifying a color of portions of theservice graph, increase the size of nodes of the service graph, modifythe arc extending between nodes, highlight text within a windowdisplaying metrics, and so forth.

At step 640, the snapshots from the set are displayed. The device maydisplay each of the snapshots in the set of snapshots in sequencecorresponding to time increments within the predetermined time period ofthe time of the anomaly. The device may display the snapshots with thehighlights in the service graph(s). The device may display changes inthe service graph(s) over time, such as differences in network traffic,differences in status of microservices, and so forth, which may bereflected in the service graphs. The device may display the snapshotsresponsive to receiving a request by the administrator. The device maydisplay the snapshots responsive to detecting the anomaly. The devicemay display the snapshots responsive to identifying the set ofsnapshots. The device may display the snapshots by transmitting a signalto a client device corresponding to a user (such as an administrator).The signal may automatically trigger generation of a user interface fordisplaying the snapshots. The signal may be a notification. As such, thedevice may provide a notification of the anomaly to the client device.The notification may automatically display on the client device. Thenotification may include buttons for launching a user interfaceincluding the set of snapshots which are displayed in sequence. Uponselection of the button by a user on the client device, the device maytrigger automatically displaying the set of snapshots. In someimplementations, the device may receive a request for displaying the setof snapshots. The device may receive the request responsive to the useridentifying an incident or anomaly. The device may automatically displaythe set of snapshots responsive to receiving the request (e.g., from theclient device upon which the user initiated the request). The device mayautomatically display the set of snapshots (or transmit a notificationfor displaying the set of snapshots) at various intervals, such as oncean hour, once a day, once a week, and so forth. The user may play thesnapshots in sequence, rewind or fast forward the snapshots, pause thesnapshots and so forth using corresponding buttons on the userinterface. The user may monitor network conditions of the networkelements, services, and/or microservices leading up to, during, andfollowing the anomaly by controlling the user interface to display thesnapshots. The user may diagnose and resolve issues with theservices/microservices based on the monitored network conditions asreflected in the snapshots.

Various elements, which are described herein in the context of one ormore embodiments, may be provided separately or in any suitablesubcombination. For example, the processes described herein may beimplemented in hardware, software, or a combination thereof. Further,the processes described herein are not limited to the specificembodiments described. For example, the processes described herein arenot limited to the specific processing order described herein and,rather, process blocks may be re-ordered, combined, removed, orperformed in parallel or in serial, as necessary, to achieve the resultsset forth herein.

It will be further understood that various changes in the details,materials, and arrangements of the parts that have been described andillustrated herein may be made by those skilled in the art withoutdeparting from the scope of the following claims.

We claim:
 1. A method for displaying a service graph in association witha time of a detected anomaly, the method comprising: (a) storing, by adevice, a plurality of snapshots of a service graph of a plurality ofmicroservices generated for each of a plurality time increments over atime period, each of the plurality of snapshots of the service graphscomprising metrics at a respective time increment from execution of eachof the plurality of microservices; (b) detecting, by the device, ananomaly with operation of one or more microservices of the plurality ofservices; (c) identifying, by the device, a set of snapshots from theplurality of snapshots of the service graph within a predetermined timeperiod of a time of the anomaly; and (d) displaying, by the device, eachof the snapshots in the set of snapshots in sequence corresponding totime increments within the predetermined time period of the time of theanomaly.
 2. The method of claim 1, wherein the predetermined time periodcomprises at least one of an amount of time before the time of theanomaly or an amount of time after the time of the anomaly.
 3. Themethod of claim 1, further comprising determining, by the device, one ormore highlights of the service associated with the detected anomaly. 4.The method of claim 3, wherein (d) further comprises displaying, by thedevice, the one or more highlights in the service graph displayed viathe set of snapshots.
 5. The method of claim 1, further comprisingidentifying, by the device, one or more changes to one of a status ormetric of the one or more microservices within the predetermined timeperiod of the time of the anomaly.
 6. The method of claim 5, wherein (d)further comprises displaying, by the device, the one or more changes inthe service graph displayed via the set of snapshots.
 7. The method ofclaim 1, wherein (a) further comprises establishing, by the device ateach of the time increments, metrics for the plurality of microservices.8. The method of claim 7, further comprising storing, by the device,metrics at each of the time increments with each snapshot of theplurality of snapshots.
 9. The method of claim 1, wherein (b) furthercomprises providing, by the device responsive to the detection, anotification of the anomaly.
 10. The method of claim 1, furthercomprising receiving, by the device, a request for the service graph atthe time of the incident and responsive to the request, displaying oneor more of the set of snapshots.
 11. A system for displaying a servicegraph in association with a time of a detected anomaly, the systemcomprising: a device comprising a plurality of processors coupled tomemory and configured to: store a plurality of snapshots of a servicegraph of a plurality of microservices generated for each of a pluralitytime increments over a time period, each of the plurality of snapshotsof the service graphs comprising metrics at a respective time incrementfrom execution of each of the plurality of microservices; detect ananomaly with operation of one or more microservices of the plurality ofservices; identify a set of snapshots from the plurality of snapshots ofthe service graph within a predetermined time period of a time of theanomaly; and display each of the snapshots in the set of snapshots insequence corresponding to time increments within the predetermined timeperiod of the time of the anomaly.
 12. The system of claim 11, whereinthe predetermined time period comprises at least one of an amount oftime before the time of the anomaly or an amount of time after the timeof the anomaly.
 13. The system of claim 11, further comprisingdetermining, by the device, one or more highlights of the serviceassociated with the detected anomaly.
 14. The system of claim 13,wherein (d) further comprises displaying, by the device, the one or morehighlights in the service graph displayed via the set of snapshots. 15.The system of claim 11, further comprising identifying, by the device,one or more changes to one of a status or metric of the one or moremicroservices within the predetermined time period of the time of theanomaly.
 16. The system of claim 15, wherein (d) further comprisesdisplaying, by the device, the one or more changes in the service graphdisplayed via the set of snapshots.
 17. The system of claim 11, wherein(a) further comprises establishing, by the device at each of the timeincrements, metrics for the plurality of microservices.
 18. The systemof claim 17, further comprising storing, by the device, metrics at eachof the time increments with each snapshot of the plurality of snapshots.19. The system of claim 11, wherein (b) further comprises providing, bythe device responsive to the detection, a notification of the anomaly.20. The system of claim 11, further comprising receiving, by the device,a request for the service graph at the time of the incident andresponsive to the request, displaying one or more of the set ofsnapshots.